IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v175y2018icp225-233.html
   My bibliography  Save this article

Failure rate updates using condition-based prognostics in probabilistic safety assessments

Author

Listed:
  • Kim, Hyeonmin
  • Kim, Jung Taek
  • Heo, Gyunyoung

Abstract

Probabilistic safety assessment (PSA) performs a key role in the safety assessment of nuclear power plants. In this study, in order to further utilize PSA, a new methodology was developed by incorporating the idea of condition-based prognostics to analyze plant-specific aging effects. In conventional PSA, aging effects are usually excluded because they are considered in separate aging management programs. Although aging effects are reflected, their quantification is generally conducted using generic data. Condition-based prognostics utilizes plant-specific data from condition-monitoring systems and quantifies prediction uncertainties. As a case study, the initiating event frequency of a steam generator tube rupture was demonstrated to show the update process by reflecting crack observation in the steam generator tubes and the effects of its maintenance. Furthermore, the variation to the total core damage frequency was also calculated by considering the availability of safety systems according to maintenance status, which reverts a system to its original state before aging effects.

Suggested Citation

  • Kim, Hyeonmin & Kim, Jung Taek & Heo, Gyunyoung, 2018. "Failure rate updates using condition-based prognostics in probabilistic safety assessments," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 225-233.
  • Handle: RePEc:eee:reensy:v:175:y:2018:i:c:p:225-233
    DOI: 10.1016/j.ress.2018.03.022
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832017310359
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2018.03.022?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Khakzad, Nima & Khan, Faisal & Amyotte, Paul, 2011. "Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches," Reliability Engineering and System Safety, Elsevier, vol. 96(8), pages 925-932.
    2. An, Dawn & Kim, Nam H. & Choi, Joo-Ho, 2015. "Practical options for selecting data-driven or physics-based prognostics algorithms with reviews," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 223-236.
    3. Yu, Hongyang & Khan, Faisal & Garaniya, Vikram, 2015. "Risk-based fault detection using Self-Organizing Map," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 82-96.
    4. Ha, Jong M. & Oh, Hyunseok & Park, Jungho & Youn, Byeng D., 2017. "Classification of operating conditions of wind turbines for a class-wise condition monitoring strategy," Renewable Energy, Elsevier, vol. 103(C), pages 594-605.
    5. Zio, Enrico & Peloni, Giovanni, 2011. "Particle filtering prognostic estimation of the remaining useful life of nonlinear components," Reliability Engineering and System Safety, Elsevier, vol. 96(3), pages 403-409.
    6. Chiachío, Juan & Chiachío, Manuel & Sankararaman, Shankar & Saxena, Abhinav & Goebel, Kai, 2015. "Condition-based prediction of time-dependent reliability in composites," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 134-147.
    7. Enrico Zio, 2013. "Monte Carlo Simulation: The Method," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 19-58, Springer.
    8. Enrico Zio, 2013. "The Monte Carlo Simulation Method for System Reliability and Risk Analysis," Springer Series in Reliability Engineering, Springer, edition 127, number 978-1-4471-4588-2, August.
    9. Enrico Zio, 2013. "System Reliability and Risk Analysis by Monte Carlo Simulation," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 59-81, Springer.
    10. Chiachío, Manuel & Chiachío, Juan & Sankararaman, Shankar & Goebel, Kai & Andrews, John, 2017. "A new algorithm for prognostics using Subset Simulation," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 189-199.
    11. Jouin, Marine & Gouriveau, Rafael & Hissel, Daniel & Péra, Marie-Cécile & Zerhouni, Noureddine, 2016. "Degradations analysis and aging modeling for health assessment and prognostics of PEMFC," Reliability Engineering and System Safety, Elsevier, vol. 148(C), pages 78-95.
    12. Zio, E., 2009. "Reliability engineering: Old problems and new challenges," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 125-141.
    13. Karanki, Durga Rao & Kim, Tae-Wan & Dang, Vinh N., 2015. "A dynamic event tree informed approach to probabilistic accident sequence modeling: Dynamics and variabilities in medium LOCA," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 78-91.
    14. Enrico Zio, 2013. "System Reliability and Risk Analysis," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 7-17, Springer.
    15. Liu, Jie & Zio, Enrico, 2017. "System dynamic reliability assessment and failure prognostics," Reliability Engineering and System Safety, Elsevier, vol. 160(C), pages 21-36.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chang, Yang & Fang, Huajing, 2019. "A hybrid prognostic method for system degradation based on particle filter and relevance vector machine," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 51-63.
    2. Hoseyni, Seyed Mojtaba & Di Maio, Francesco & Zio, Enrico, 2019. "Condition-based probabilistic safety assessment for maintenance decision making regarding a nuclear power plant steam generator undergoing multiple degradation mechanisms," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    3. Li, Rui & Verhagen, Wim J.C. & Curran, Richard, 2020. "A systematic methodology for Prognostic and Health Management system architecture definition," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    4. Moradi, Ramin & Groth, Katrina M., 2020. "Modernizing risk assessment: A systematic integration of PRA and PHM techniques," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    5. D'Urso, Diego & Chiacchio, Ferdinando & Cavalieri, Salvatore & Gambadoro, Salvatore & Khodayee, Soheyl Moheb, 2024. "Predictive maintenance of standalone steel industrial components powered by a dynamic reliability digital twin model with artificial intelligence," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    6. Hanna Hrinchenko & Viktor Koval & Nadiia Shmygol & Oleksandr Sydorov & Oksana Tsimoshynska & Dominika Matuszewska, 2023. "Approaches to Sustainable Energy Management in Ensuring Safety of Power Equipment Operation," Energies, MDPI, vol. 16(18), pages 1-15, September.
    7. Mandelli, Diego & Wang, Congjian & Agarwal, Vivek & Lin, Linyu & Manjunatha, Koushik A., 2024. "Reliability modeling in a predictive maintenance context: A margin-based approach," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    8. Martón, I. & Sánchez, A.I. & Carlos, S. & Mullor, R. & Martorell, S., 2023. "Prognosis of wear-out effect on of safety equipment reliability for nuclear power plants long-term safe operation," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    9. Xing, Jinduo & Zeng, Zhiguo & Zio, Enrico, 2019. "A framework for dynamic risk assessment with condition monitoring data and inspection data," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    10. Lewis, Austin D. & Groth, Katrina M., 2022. "Metrics for evaluating the performance of complex engineering system health monitoring models," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    11. Xing, Jinduo & Zeng, Zhiguo & Zio, Enrico, 2020. "Joint optimization of safety barriers for enhancing business continuity of nuclear power plants against steam generator tube ruptures accidents," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    12. Jagtap, Hanumant P. & Bewoor, Anand K. & Kumar, Ravinder & Ahmadi, Mohammad Hossein & Chen, Lingen, 2020. "Performance analysis and availability optimization to improve maintenance schedule for the turbo-generator subsystem of a thermal power plant using particle swarm optimization," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    13. Lewis, Austin D. & Groth, Katrina M., 2023. "A comparison of DBN model performance in SIPPRA health monitoring based on different data stream discretization methods," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    14. Kowal, Karol, 2022. "Lifetime reliability and availability simulation for the electrical system of HTTR coupled to the electricity-hydrogen cogeneration plant," Reliability Engineering and System Safety, Elsevier, vol. 223(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Fan & Li, Heng, 2018. "System reliability under prescribed marginals and correlations: Are we correct about the effect of correlations?," Reliability Engineering and System Safety, Elsevier, vol. 173(C), pages 94-104.
    2. Penttinen, Jussi-Pekka & Niemi, Arto & Gutleber, Johannes & Koskinen, Kari T. & Coatanéa, Eric & Laitinen, Jouko, 2019. "An open modelling approach for availability and reliability of systems," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 387-399.
    3. Martin Folch-Calvo & Francisco Brocal-Fernández & Cristina González-Gaya & Miguel A. Sebastián, 2020. "Analysis and Characterization of Risk Methodologies Applied to Industrial Parks," Sustainability, MDPI, vol. 12(18), pages 1-35, September.
    4. Mohammad Nadjafi & Mohammad Ali Farsi & Hossein Jabbari, 2017. "Reliability analysis of multi-state emergency detection system using simulation approach based on fuzzy failure rate," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(3), pages 532-541, September.
    5. Salomon, Julian & Winnewisser, Niklas & Wei, Pengfei & Broggi, Matteo & Beer, Michael, 2021. "Efficient reliability analysis of complex systems in consideration of imprecision," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    6. Chiachío, Manuel & Chiachío, Juan & Sankararaman, Shankar & Goebel, Kai & Andrews, John, 2017. "A new algorithm for prognostics using Subset Simulation," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 189-199.
    7. Aizpurua, J.I. & Catterson, V.M. & Papadopoulos, Y. & Chiacchio, F. & D'Urso, D., 2017. "Supporting group maintenance through prognostics-enhanced dynamic dependability prediction," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 171-188.
    8. Michele Compare & Francesco Di Maio & Enrico Zio & Fausto Carlevaro & Sara Mattafirri, 2016. "Improving scheduled maintenance by missing data reconstruction: A double-loop Monte Carlo approach," Journal of Risk and Reliability, , vol. 230(5), pages 502-511, October.
    9. Di Maio, Francesco & Pettorossi, Chiara & Zio, Enrico, 2023. "Entropy-driven Monte Carlo simulation method for approximating the survival signature of complex infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    10. Tito G. Amaral & Vitor Fernão Pires & Armando Cordeiro & Daniel Foito & João F. Martins & Julia Yamnenko & Tetyana Tereschenko & Liudmyla Laikova & Ihor Fedin, 2023. "Incipient Fault Diagnosis of a Grid-Connected T-Type Multilevel Inverter Using Multilayer Perceptron and Walsh Transform," Energies, MDPI, vol. 16(6), pages 1-18, March.
    11. Rocco, Claudio M. & Moronta, José & Ramirez-Marquez, José E. & Barker, Kash, 2017. "Effects of multi-state links in network community detection," Reliability Engineering and System Safety, Elsevier, vol. 163(C), pages 46-56.
    12. Babykina, Génia & Brînzei, Nicolae & Aubry, Jean-François & Deleuze, Gilles, 2016. "Modeling and simulation of a controlled steam generator in the context of dynamic reliability using a Stochastic Hybrid Automaton," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 115-136.
    13. Compare, Michele & Bellani, Luca & Zio, Enrico, 2017. "Reliability model of a component equipped with PHM capabilities," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 4-11.
    14. Gascard, Eric & Simeu-Abazi, Zineb, 2018. "Quantitative Analysis of Dynamic Fault Trees by means of Monte Carlo Simulations: Event-Driven Simulation Approach," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 487-504.
    15. Shiyu Chen & Wei Wang & Enrico Zio, 2021. "A Simulation-Based Multi-Objective Optimization Framework for the Production Planning in Energy Supply Chains," Energies, MDPI, vol. 14(9), pages 1-27, May.
    16. Cadini, F. & Gioletta, A., 2016. "A Bayesian Monte Carlo-based algorithm for the estimation of small failure probabilities of systems affected by uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 15-27.
    17. Turati, Pietro & Pedroni, Nicola & Zio, Enrico, 2016. "Advanced RESTART method for the estimation of the probability of failure of highly reliable hybrid dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 117-126.
    18. Sergio Copiello, 2020. "Business as Usual with Article Processing Charges in the Transition towards OA Publishing: A Case Study Based on Elsevier," Publications, MDPI, vol. 8(1), pages 1-14, January.
    19. Shafiee, Mahmood & Finkelstein, Maxim & Bérenguer, Christophe, 2015. "An opportunistic condition-based maintenance policy for offshore wind turbine blades subjected to degradation and environmental shocks," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 463-471.
    20. Su, Huai & Zhang, Jinjun & Zio, Enrico & Yang, Nan & Li, Xueyi & Zhang, Zongjie, 2018. "An integrated systemic method for supply reliability assessment of natural gas pipeline networks," Applied Energy, Elsevier, vol. 209(C), pages 489-501.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:175:y:2018:i:c:p:225-233. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.